abstract = "In recent decades, artificial intelligence (AI)
techniques have been pronounced as a branch of computer
science to model wide range of hydrological phenomena.
A number of researches have been still comparing these
techniques in order to find more effective approaches
in terms of accuracy and applicability. In this study,
we examined the ability of linear genetic programming
(LGP) technique to model successive-station monthly
streamflow process, as an applied alternative for
streamflow prediction. A comparative efficiency study
between LGP and three different artificial neural
network algorithms, namely feed forward back
propagation (FFBP), generalised regression neural
networks (GRNN), and radial basis function (RBF), has
also been presented in this study. For this aim,
firstly, we put forward six different
successive-station monthly streamflow prediction
scenarios subjected to training by LGP and FFBP using
the field data recorded at two gauging stations on
Coruh River, Turkey. Based on Nash-Sutcliffe and root
mean squared error measures, we then compared the
efficiency of these techniques and selected the best
prediction scenario. Eventually, GRNN and RBF
algorithms were used to restructure the selected
scenario and to compare with corresponding FFBP and
LGP. Our results indicated the promising role of LGP
for successive-station monthly streamflow prediction
providing more accurate results than those of all the
ANN algorithms. We found an explicit LGP-based
expression evolved by only the basic arithmetic
functions as the best prediction model for the river,
which uses the records of the both target and upstream
stations.",